Shared Control Research Articles

Intent-based Task-Oriented Shared Control for Intuitive Telemanipulation

The unique challenges in shared control for telemanipulation-beyond teleoperation-include physical discrepancy between human and robot hands and the fine manipulation constraints needed for task success. We present an intuitive shared-control strategy that generates robotic grasp poses better suited for human perception of success and feeling of control while ensuring a stable grasp for task success. The robot’s motion follows an arbitration between following the user’s motion constraints and accomplishing the inferred task. The arbitration adapts based on the physical discrepancy between the human and robot hands. We have conducted a user study with a telemanipulation scenario to analyze the effects of task predictability, following, and user preference. The results demonstrated that intent-based approaches provide advantages over direct motion mapping strategies.

Shared Control Design of a Walking-Assistant Robot

Walking-assistant robots serve as a vital aid for individuals with mobility challenges, enhancing their independence and mobility. Shared control systems combine user input and robotic intelligence to ensure safe, adaptable, and intuitive navigation. This paper presents a shared control framework for a walking-assistant robot, focusing on user intention prediction and real-time obstacle avoidance. A Python-based simulation demonstrates the system's performance in a scaled-up environment with larger obstacles and a broader robot trajectory.

Dynamic Movement Primitives-Based Human Action Prediction and Shared Control for Bilateral Robot Teleoperation

Dynamic Movement Primitives-Based Human Action Prediction and Shared Control for Bilateral Robot Teleoperation

SGR-AutoLap: Surgical gesture recognition-based autonomous laparoscope control for human-robot shared control in semi-autonomous minimally invasive surgical robot

Purpose This paper aims to investigate the problem of vision based autonomous laparoscope control, which can serve as the primary function for semi-autonomous minimally invasive surgical robot system. Providing the surgical gesture recognition information is a fundamental key component for enabling intelligent context-aware assistance in autonomous laparoscope control task. While significant advances have been made in recent years, how to effectively carry out the efficient integration of surgical gesture recognition and autonomous laparoscope control algorithms for robotic assisted minimally invasive surgical robot system is still an open and challenging topic. Design/methodology/approach The authors demonstrate a novel surgeon in-loop semi-autonomous robotic-assisted minimally invasive surgery framework by integrating the surgical gesture recognition and autonomous laparoscope control tasks. Specifically, they explore using a transformer-based deep convolutional neural network to effectively recognize the current surgical gesture. Next, they propose an autonomous laparoscope control model to provide optimal field of view which is in line with surgeon intra-operation preferences. Findings The effectiveness of this surgical gesture recognition methodology is demonstrated on the public JIGSAWS and Cholec80 data sets, outperforming the comparable state-of-the-art methods. Furthermore, the authors have validated the effectiveness of the proposed semi-autonomous framework on the developed HUAQUE surgical robot platforms. Originality/value This study demonstrates the feasibility to perform cognitive assistant human–robot shared control for semi-autonomous robotic-assisted minimally invasive surgery, contributing to the reference for further surgical intelligence in computer-assisted intervention systems.

Towards equitable shared control

Towards equitable shared control

Data Ownership in the AI-Powered Integrative Health Care Landscape.

In the rapidly advancing landscape of artificial intelligence (AI) within integrative health care (IHC), the issue of data ownership has become pivotal. This study explores the intricate dynamics of data ownership in the context of IHC and the AI era, presenting the novel Collaborative Healthcare Data Ownership (CHDO) framework. The analysis delves into the multifaceted nature of data ownership, involving patients, providers, researchers, and AI developers, and addresses challenges such as ambiguous consent, attribution of insights, and international inconsistencies. Examining various ownership models, including privatization and communization postulates, as well as distributed access control, data trusts, and blockchain technology, the study assesses their potential and limitations. The proposed CHDO framework emphasizes shared ownership, defined access and control, and transparent governance, providing a promising avenue for responsible and collaborative AI integration in IHC. This comprehensive analysis offers valuable insights into the complex landscape of data ownership in IHC and the AI era, potentially paving the way for ethical and sustainable advancements in data-driven health care.

Robotic telemanipulation with EMG-driven strategy-assisted shared control method

Robotic telemanipulation with EMG-driven strategy-assisted shared control method

Robust Shared Control With Stable Contact Servoing for Enhanced Object Transportation by Telerobotic Bimanual Mobile Manipulators

ABSTRACTBimanual mobile manipulation significantly enhances the capabilities of field robots that interact with diverse dynamic environments and expands the range of possible manipulation actions. This paper introduces a robust shared control architecture for remote manipulation of the Collaborative Dual‐arm Robot Manipulator (CURI), aimed at stable object transportation. Given that stable contact is pivotal for successful object transportation, our focus is on stabilizing the contact between the robot end‐effectors and the object using a contact servoing strategy. To maintain stable contact, it is essential that the contact wrenches consistently stay within the predefined friction cones throughout the task. We propose a novel shared control strategy to stabilize the contact using the contact parameterization model. This model formulates the contact stability manifold using a set of unconstrained reparameterized variables through a bijective mapping function, ensuring that the controller derived from these unconstrained reparameterized variables consistently maintains contact stability. The efficacy of the proposed approach is convincingly demonstrated through a series of experiments involving representative bimanual transportation tasks within a logistics context. Both theoretical analysis and experimental validations confirm that our proposed control strategy not only ensures the stable transportation of objects by CURI but also substantially enhances the overall robustness of the shared control system.

Human-Vehicle Shared Steering Control for Obstacle Avoidance: A Reference-Free Approach With Reinforcement Learning

Human-Vehicle Shared Steering Control for Obstacle Avoidance: A Reference-Free Approach With Reinforcement Learning

Exploring Shared Perception and Control in Cooperative Vehicle-Intersection Systems: A Review

Exploring Shared Perception and Control in Cooperative Vehicle-Intersection Systems: A Review

Safety-Aware Human-in-the-Loop Reinforcement Learning With Shared Control for Autonomous Driving

Safety-Aware Human-in-the-Loop Reinforcement Learning With Shared Control for Autonomous Driving

Imitation Learning and Teleoperation Shared Control With Unit Tangent Fuzzy Movement Primitives

Imitation Learning and Teleoperation Shared Control With Unit Tangent Fuzzy Movement Primitives

Driver–Automated Cooperation Driving Authority Optimization Framework for Shared Steering Control

In this paper, we introduce the preview-follower theory for modeling the trajectory-tracking controller of an automated system using model predictive control (MPC). The primary contribution of this research lies in enhancing tracking accuracy and driving safety when the driver and automated system share similar driving intentions, while also enabling a rapid transfer of driving authority to the human driver in cases of differing intentions. To verify the effectiveness of the proposed driving authority optimization framework, both simulation and driver-in-the-loop experiments were conducted under conditions of consistent and inconsistent driving intentions between the human driver and the autonomous driving system. The results of both experiments demonstrated that the proposed shared steering cooperative control and driving authority optimization framework not only significantly improves vehicle tracking accuracy but also promptly aligns with the driver’s intentions.

Connectome-based Predictive Models of General and Specific Cognitive Control.

Cognitive control, the ability to adapt thoughts and actions to shifting contexts and goals, is composed primarily of three distinct yet interrelated components: Inhibition, Shifting, and Updating. While prior research has examined the nature of different cognitive components as well as their inter-relationships, fewer studies examined whole-brain connectivity to predict individual differences for the three cognitive components and associated tasks. Here, using the Connectome-based Predictive Modelling (CPM) approach and open-access data from the Human Connectome Project, we built brain network models to successfully predict individual performance differences on the Flanker task, the Dimensional Change Card Sort task, and the 2-Back task, each putatively corresponding to Inhibition, Shifting, and Updating. We focused on grayordinate fMRI data collected during the 2-Back tasks after confirming superior predictive performance over resting-state and volumetric data. High cross-task prediction accuracy as well as joint recruitment of canonical networks, such as the frontoparietal and default-mode networks, suggest the existence of a common cognitive control factor. To directly investigate the relationships among the three cognitive control components, we developed new measures to disentangle their shared and unique aspects. Our analysis confirmed that a shared control component can be well predicted from functional connectivity patterns densely located around the frontoparietal, default-mode and dorsal attention networks. In contrast, the Shifting-specific and Inhibition-specific components exhibited lower cross-prediction performance, indicating their distinct and specialized roles. Notably, the Updating-specific component showed significant cross-prediction with the general control factor, suggesting its central role in cognitive control. Given the limitation that individual behavioral measures do not purely reflect the intended cognitive constructs, our study demonstrates the need to distinguish between common and specific components of cognitive control.

Lifespan of male and female APP/PS1 and APPNL-F/NL-F mouse models of Alzheimer's disease.

Alzheimer's disease (AD) disproportionately affects women, yet most preclinical research studies are male-centric. We performed lifespan analyses of male and female AD mouse models (APP/PS1 and APPNL-F/NL-F) and their shared genetic background control (C57BL/6). Survival curves support significant sex differences between within genotypes. Minimal longevity revealed increased age in male APP/PS1, and decreased age in APPNL-F/NL-F mice. Maximal longevity revealed an increased average age in males. Furthermore, median lifespan differed between sex and genotype. This study supports sexual dimorphic survival in two mouse models of AD, emphasizing the need to examine mechanisms and treatments in both sexes.

Steering authority allocation strategy for human-machine shared control based on driver take-over feasibility

Misoperation when a driver is distracted, e.g., turning the steering wheel unconsciously, preventing the machine from turning the steering wheel, etc., can lead to a false take-over of the vehicle, which poses a danger to shared control. Therefore, it is essential to evaluate the feasibility of driver take-over in distraction status. This paper proposes a steering authority allocation strategy based on driver take-over feasibility to reduce the interference of distracted driver behavior on shared control. First, a driver take-over feasibility framework is established based on quantified driver distraction levels and take-over intention. Then the shared control system is established based on a linear parameter-varying vehicle model and non-cooperative game theory to suppress the interference of time-varying speed and parameter uncertainty on shared control. Subsequently, the steering authority allocation strategy is constructed in conjunction with driver take-over feasibility and environmental risk evaluation. The proposed sigmoid-based mapping function ensures fast and smooth authority allocation. Finally, a series of driver-in-the-loop experiments are conducted to validate the effectiveness of our proposed strategy across various driving scenarios, and the results demonstrate that our strategy has superior performance than other advanced shared control schemes in reducing steering burden, improving driving safety, and enhancing vehicle stability, etc.

Interactive strategy of shared control vehicle and automated vehicles based on adaptive non-cooperative game

Considering the complex traffic environment in the future, an interactive strategy between human-machine shared control vehicle (HSCV) and automated vehicles (AVs) based on the adaptive non-cooperative game (NCG) theory is proposed to ensure the safety of vehicles interaction control involving drivers with different characteristics. In HSCV, first, considering the environmental risk potential field (ERPF) and the human-machine conflict coefficient, an adaptive weight distribution method is designed to realize the weight distribution between a driver and an automation system. Subsequently, the steering angle command imposed by a driver is introduced into the game solving process between HSCV and AVs, enabling adaptive dynamic game through weight adjustment. During V2V interaction, based on NCG and distributed model predictive control, a motion planning controller (MOPC) is designed to integrate the optimal solution problem of path planning and tracking control and solve the interaction problem with constraints. Two typical interactive scenarios are used to verify the feasibility of the strategy. The results demonstrate that the interaction strategy considering the different drivers’ operating commands not only realizes the interaction between a driver and an ego vehicle but also realizes the interaction between an ego vehicle and nearby vehicles to ensure the safety of vehicles.

Distance and Preference for Full Versus Shared Control: The Moderating Role of Decision-Maker Self-Construal

Previous research on the impact of distance on the choice between full versus shared control international market entry has produced inconsistent results. The authors suggest that the characteristics of the decision-maker may be an important boundary condition, which helps explain this inconsistency. Specifically, the authors examine how differences in decision-maker self-construal influence the effects of distance on entry mode decisions. To investigate the phenomenon, the authors use multiple methods to conduct three separate studies, including two experiments and a meta-analysis. The results offer consistent evidence that the distance–entry mode relationship is stronger for managers with an independent self-construal than for managers with an interdependent self-construal.

A shared robot control system combining augmented reality and motor imagery brain–computer interfaces with eye tracking

Objective: Brain-computer interface (BCI) control systems monitor neural activity to detect the user's intentions, enabling device control through mental imagery. Despite their potential, decoding neural activity in real-world conditions poses significant challenges, making BCIs currently impractical compared to traditional interaction methods. This study introduces a novel motor imagery (MI) BCI control strategy for operating a physically assistive robotic arm, addressing the difficulties of MI decoding from electroencephalogram (EEG) signals, which are inherently non-stationary and vary across individuals.
Approach: A proof-of-concept BCI control system was developed using commercially available hardware, integrating MI with eye tracking in an augmented reality (AR) user interface to facilitate a shared control approach. This system proposes actions based on the user's gaze, enabling selection through imagined movements. A user study was conducted to evaluate the system's usability, focusing on its effectiveness and efficiency.
Main results:Participants performed tasks that simulated everyday activities with the robotic arm, demonstrating the shared control system's feasibility and practicality in real-world scenarios. Despite low online decoding performance (mean accuracy: 0.52 9, F1: 0.29, Cohen's Kappa: 0.12), participants achieved a mean success rate of 0.83 in the final phase of the user study when given 15 minutes to complete the evaluation tasks. The success rate dropped below 0.5 when a 5-minute cutoff time was selected.
Significance: These results indicate that integrating AR and eye tracking can significantly enhance the usability of BCI systems, despite the complexities of MI-EEG decoding. While efficiency is still low, the effectiveness of our approach was verified. This suggests that BCI systems have the potential to become a viable interaction modality for everyday applications
in the future.

Human Cognitive Learning in Shared Control via Differential Game With Bounded Rationality and Incomplete Information

Human Cognitive Learning in Shared Control via Differential Game With Bounded Rationality and Incomplete Information